Many algorithms for automated analysis of medical images, such as X-ray images, have been suggested. In general, the automated analyses rely upon segmentation and/or comparison to a model. These automated analyses have been applied to analyses of bone mineral density, fractures and lesions. However, previously available automated analyses can be influenced by distortion caused by an angle of incidence of X-ray beams with respect to an imaged bone. FIGS. 9A and 9B show that a change in the angle of incidence from −10 degrees (FIG. 9A) to +30 degrees (FIG. 9B) can cause significant changes in important measured anatomic features. For example, the femur head offset (indicated by the arrow) is 5.2 cm at −10 degrees and 2.1 cm at +30 degrees. The head shaft angles (θ) are 135° and 153°, respectively. As a result, even when automated image analyses accurately measure selected image parameters, the automatically measured parameters may be difficult to evaluate.
Manual analysis by a radiologist is subject to similar influence from angle of incidence, although to a lesser degree. An expert radiologist may be able to judge an approximate angle of incidence for a particular image and mentally correct measurements. However, it may take many years of clinical experience to acquire this level of experience. Also, a radiologist with vast experience in evaluating hip X-rays may have significantly less experience with images of other body portions (e.g ankle or spine). Alternatively or additionally, an “eyeball” correction tends to be more qualitative than quantitative.
It is known to register a 3D image (e.g. CT scan) of an organ from a specific patient onto a 2D image acquired from the same patient (e.g., an X-ray image). This permits information from CT images to be used during interventional procedures by registering the CT scan to an intra-operative X-ray fluoroscopy image. This method registers a fluoroscopy image with respect to a CT scan (“An Overview of Medical Image Registration Models” Maintz, J. B. A., & Viergever, M. A. (1998) An Overview of Medical Image Registration Methods. UU-CS (Ext rep. 1998-22, Utrecht University Information and Computing Sciences, Utrecht, the Netherlands) and also “Fast Intensity-based 2D-3D Image Registration of Clinical Data Using Light Fields” (2003) Daniel B. Russakoff, Torsten Rohlfing, Calvin Maurer; the contents of which are each fully incorporated herein by reference). Methods described in these references employ a 3D scan from a subject to evaluate a 2D image from the same subject and do not consider determining an angle of incidence of a 2D image without acquiring a 3D scan from the same patient.
Automatic contour and/or segmentation analysis of medical images are generally known in the art.
Chen et al. describe a contour analysis model which relies upon an algorithm based upon a plurality of 2D images with manually defined Femur contours to determine Femur contours in additional input images. (Ying Chen et al. (2005) “Automatic Extraction of Femur Contours from Hip X-Ray Images”, CVBIA. 2005: 200-209; the contents of which are fully incorporated herein by reference).
Exemplary segmentation models are described by Kass et al. and by Long and Thoma (M. Kass et al. (1987) “Snakes: Active contour models”. In First International Conference on Computer Vision; London; pages 259-268; L. R. Long and G. R. Thoma (1999) “Segmentation and feature extraction of cervical spine X-ray images” Proc. SPIE Medical Imaging: Image Processing, San Diego, Calif., 3661:1037-1046 the contents of which are each fully incorporated herein by reference).
Registration of one image with respect to another has been attempted by a variety of methods. This subject is reviewed in “Image registration methods: a survey”, by Barbara Zitova and Jan Flusser (Image and Vision Computing 21 (2003) 977-1000). The contents of this article are fully incorporated herein by reference.
U.S. Pat. No. 6,990,229 describes a system for alignment and simultaneous display of 2 or more 3D medical images. The disclosure of this patent is fully incorporated herein by reference.
U.S. Pat. No. 6,075,879 describes systems and methods for computer aided detection of suspicious lesions, primarily in breast tissue. This patent describes comparison of two 2D images acquired from different known angles. The disclosure of this patent is fully incorporated herein by reference.
Horn and Brooks describe reconstruction of the shape of a smooth object from a given single gray level image (B. K. P. Horn and M. J. Brooks (1989) “Shape from Shading” MIT Press, Cambridge Mass). According to Horn and Brooks, the shading of an object is a function of the projection of the surface normal onto the light source direction. However, shape from shading methods as described by Horn are useful primarily for images acquired with light, which is reflected. The contents of this article are fully incorporated herein by reference.
U.S. Pat. No. 6,206,566 discloses a system for determining the angle of incident X-ray beams using X-ray positive marks deployed during image acquisition. All methods described in this patent must be implemented during image capture. The disclosure of this patent is fully incorporated herein by reference.